A negative binomial latent factor model for paired microbiome sequencing data

We propose a latent factor model for microbiome sequencing data collected from multiple sites. It captures the presumptive underlying cross-site associations without compromising estimation accuracy or inference efficiency in the absence of such associations. In addition, our proposed model improves predictive performance by enabling the prediction of microbial abundance at one site based on observations from another. We also provide an extended framework that allows for clustering of subjects (samples) and cluster-specific levels of paired association. Under this extended framework, clusters can be classified according to their association strengths.